Prevalence of Drug Use in Injured British Columbia Drivers
Bibliographic record
Abstract
Motor vehicle crashes (MVCs) due to impaired driving are a leading cause of preventable injury and death and Canadians perceive impaired driving as the most important road safety issue today. Alcohol is well known to impair driving performance and crash risk increases with the amount consumed. Coroners’ studies and trauma center data show that alcohol is involved in about one third of MVCs resulting in serious injury or death. Many illicit drugs such as cannabis, over the counter medications such as antihistamines, and prescription medications such as benzodiazepines, also impair the psychomotor skills required for safe driving. Drivers may be able to compensate for impairment by driving more slowly or engaging in fewer risky maneuvers, but epidemiological evidence suggests that cannabis and benzodiazepines do increase the risk of crashing in real world driving conditions. For other illicit drugs and for other classes of prescription medications the epidemiological evidence for increased risk of crashing is very limited. Compared to drunk driving, drug driving remains poorly understood. It is important for road safety stakeholders to know the prevalence of drug driving, which impairing drugs are most commonly used by drivers, and which drivers are most likely to use drugs. This information can help public health agencies and road safety organizations develop public education and awareness campaigns. Healthcare personnel can use this information to develop medication warnings or interventions that target high risk drivers. Police agencies can develop drugged driving enforcement campaigns that target high risk drugs and drivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".